Did socio-ecological factors drive the spatiotemporal patterns of pandemic influenza A (H1N1)?

Hu, Wenbiao, Williams, Gail, Phung, Hai, Birrell, Frances, Tong, Shilu, Mengersen, Kerrie, Huang, Xiaodong and Clements, Archie (2012) Did socio-ecological factors drive the spatiotemporal patterns of pandemic influenza A (H1N1)?. Environment International, 45 1: 39-43. doi:10.1016/j.envint.2012.03.010

Author Hu, Wenbiao
Williams, Gail
Phung, Hai
Birrell, Frances
Tong, Shilu
Mengersen, Kerrie
Huang, Xiaodong
Clements, Archie
Title Did socio-ecological factors drive the spatiotemporal patterns of pandemic influenza A (H1N1)?
Journal name Environment International   Check publisher's open access policy
ISSN 0160-4120
Publication date 2012-09
Sub-type Article (original research)
DOI 10.1016/j.envint.2012.03.010
Open Access Status
Volume 45
Issue 1
Start page 39
End page 43
Total pages 5
Place of publication Oxford, United Kingdom
Publisher Pergamon
Collection year 2013
Language eng
Formatted abstract

Pandemic influenza A (H1N1) has a significant public health impact. This study aimed to examine the effect of socio-ecological factors on the transmission of H1N1 in Brisbane, Australia.


We obtained data from Queensland Health on numbers of laboratory-confirmed daily H1N1 in Brisbane by statistical local areas (SLA) in 2009. Data on weather and socio-economic index were obtained from the Australian Bureau of Meteorology and the Australian Bureau of Statistics, respectively. A Bayesian spatial conditional autoregressive (CAR) model was used to quantify the relationship between variation of H1N1 and independent factors and to determine its spatiotemporal patterns.


Our results show that average increase in weekly H1N1 cases were 45.04% (95% credible interval (CrI): 42.63–47.43%) and 23.20% (95% CrI: 16.10–32.67%), for a 1 °C decrease in average weekly maximum temperature at a lag of one week and a 10 mm decrease in average weekly rainfall at a lag of one week, respectively. An interactive effect between temperature and rainfall on H1N1 incidence was found (changes: 0.71%; 95% CrI: 0.48–0.98%). The auto-regression term was significantly associated with H1N1 transmission (changes: 2.5%; 95% CrI: 1.39–3.72). No significant association between socio-economic indexes for areas (SEIFA) and H1N1 was observed at SLA level.


Our results demonstrate that average weekly temperature at lag of one week and rainfall at lag of one week were substantially associated with H1N1 incidence at a SLA level. The ecological factors seemed to have played an important role in H1N1 transmission cycles in Brisbane, Australia.
Keyword H1N1
Bayesian spatial conditional autoregressive
Socio-ecological factors
Q-Index Code C1
Q-Index Status Confirmed Code
Institutional Status UQ

Document type: Journal Article
Sub-type: Article (original research)
Collections: Official 2013 Collection
School of Public Health Publications
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